Unconscious Emotion Recognition based on Multi-scale Sample Entropy

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Abstract
Subliminal stimulation can trigger emotional responses and this paper analyzes their relationship based on EEG signals. According to the nonlinear characteristics of EEG signals and based on the multi-scale sample entropy, we propose a method to recognize unconscious emotion. We extract the multi-scale sample entropy of EEG signals when angry or happy face pictures are subliminally presented as eigenvalues. Then, we compute the distribution and regularity of eigenvalues. Experimental results demonstrate that the multi-scale sample entropy can be used to distinguish different emotions when different emotional pictures are subliminally presented, namely, the multi-scale sample entropy is larger when angry face pictures are subliminally presented than happy face pictures. Furthermore, the p-values of extracted features are calculated by the method of Kolmogorov-Smirnov test (KS test). The result shows that the EEG signals have significant difference (p<;0.05) for the two different emotional states and the method is effective for unconscious emotion recognition.

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